import streamlit as st
from transformers import pipeline
import soundfile as sf
import os

# Cache models to avoid reloading on every interaction
@st.cache_resource
def load_models():
    return {
        "image_to_text": pipeline("image-to-text", model="Salesforce/blip-image-captioning-base"),
        "story_gen": pipeline("text-generation", model="Qwen/Qwen2.5-1.5B-Instruct"),
        "text_to_speech": pipeline("text-to-speech", model="facebook/mms-tts-eng")
    }

# function part
def img2text(url, processor):
    text = processor(url)[0]["generated_text"]
    return text

def text2story(text, generator, max_length=150):
    prompt = f"Create a story based on: {text}"
    story = generator(
        prompt,
        max_length=max_length,
        temperature=0.7,
        do_sample=True
    )[0]['generated_text']
    return story[:1000]  # Safety truncation

def text2audio(text, synthesizer):
    audio = synthesizer(text)
    return audio

# main part
def main():
    st.set_page_config(page_title="Your Image to Audio Story", page_icon="🦜")
    st.header("Turn Your Image to Audio Story")
    
    # Load models once
    models = load_models()
    
    uploaded_file = st.file_uploader("Select an Image...", type=["jpg", "jpeg", "png"])
    
    if uploaded_file is not None:
        # Save uploaded file temporarily
        temp_path = f"temp_{uploaded_file.name}"
        with open(temp_path, "wb") as f:
            f.write(uploaded_file.getvalue())
            
        st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
        
        # Stage 1: Image to Text
        with st.spinner('Generating caption...'):
            scenario = img2text(temp_path, models["image_to_text"])
            st.subheader("Image Caption")
            st.write(scenario)
        
        # Stage 2: Text to Story
        with st.spinner('Creating story...'):
            story = text2story(scenario, models["story_gen"])
            st.subheader("Generated Story")
            st.write(story)
        
        # Stage 3: Story to Audio
        with st.spinner('Generating audio...'):
            audio = text2audio(story, models["text_to_speech"])
            sf.write("temp_audio.wav", audio["audio"], samplerate=audio["sampling_rate"])
            
            st.subheader("Audio Story")
            st.audio("temp_audio.wav")
            
        # Clean up temp files
        os.remove(temp_path)
        os.remove("temp_audio.wav")

if __name__ == "__main__":
    main()